Novel Degree-Based Topological Descriptors of Fenofibrate Using M-Polynomial

Chemical graph theory is currently expanding the use of topological indices to numerically encode chemical structure. The prediction of the characteristics provided by the chemical structure of the molecule is a key feature of these topological indices. The concepts from graph theory are presented in a brief discussion of one of its many applications to chemistry, namely, the use of topological indices in quantitative structure-activity relationship (QSAR) studies and quantitative structure-property relationship (QSPR) studies. This study uses the M-polynomial approach, a newly discovered technique, to determine the topological indices of the medication fenofibrate. With the use of degree-based topological indices, we additionally construct a few novel degree based topological descriptors of fenofibrate structure using M-polynomial. When using M-polynomials in place of degree-based indices, the computation of the topological indices can be completed relatively quickly. The topological indices are also plotted. Using M-polynomial, we compute novel formulas for the modified first Zagreb index, modified second Zagreb index, first and second hyper Zagreb indices, SK index, SK1 index, SK2 index, modified Albertson index, redefined first Zagreb index, and degree-based topological indices.

Ve-Degree, Ev-Degree, and Degree-Based Topological Indices of Fenofibrate

The molecular topology of a graph is described by topological indices, which are numerical measures. In theoretical chemistry, topological indices are numerical quantities that are used to represent the molecular topology of networks. These topological indices can be used to calculate several physical and chemical properties of chemical compounds, such as boiling point, entropy, heat generation, and vaporization enthalpy. Graph theory comes in handy when looking at the link between certain topological indices of some derived graphs. In the ongoing research, we determine ve-degree, ev-degree, and degree-based (D-based) topological indices of fenofibrate’s chemical structure. These topological indices are the Zagreb index, general Randić index, modified Zagreb index, and forgotten topological index. These indices are very helpful to study the characterization of the given structure.

Critical factors for successful management of VUCA times

Background COVID-19 pandemic exposed leadership teams to novel challenges that required many changes to their practices. This has been the most volatile, uncertain, complex and ambiguous (VUCA) times in healthcare. Interventions This brief report uses experiences at Oxford University Hospitals to propose that an organisation's culture serves as a bedrock on which management of a crisis such as the COVID-19 pandemic is reliant. The other two critical factors are partnership working and clarity of the strategic intent of the organisation. Conclusions While many of the actions described in this report are likely to be in common with those of other leadership teams across the National Health Service, some organisations seem to manage the response to the VUCA situations better than others and the three factors are repeatedly observed in these organisations. This brief report explores what actions support the three critical factors that make some organisations more resilient and their leaders' actions more effective.

Experiences, challenges and lessons learnt in medical staff redeployment during response to COVID-19

Background: The response to the COVID-19 pandemic required redeployment of large numbers of staff to avoid acute services being overwhelmed. This unprecedented, previously unplanned redeployment occurred in a rapidly changing environment. This paper describes the process of redeployment at a teaching hospital and assessment of this by the redeployed doctors and redeployment team. Objective: Identify key lessons from the redeployment process to inform resilience and future planning for further COVID-19 peaks. Methods: Redeployment team experiences and challenges were documented in real time and formal structured feedback obtained. All redeployed doctors were asked for quantitative and qualitative feedback regarding their experiences in two distinct acute areas with different approaches to staffing. Results: 63 redeployed staff and five members of the redeployment team completed feedback questionnaires. Most redeployed doctors (76%) were satisfied and had adequate support and training. Redeployment was associated with self-reported stress and anxiety in 95% with 59% describing this as moderate or greater. This was reduced by adequate communication, supervision and a sense of belonging to a firm with access to simple information making a significant difference. Awareness of and satisfaction with well-being support services was also high (71%). The redeployment team identified having a well-mixed team who met daily, an online portal and engagement with leads as the key factors for being successful. Conclusion: Redeployment in response to COVID-19 was associated with reported stress and anxiety in most redeployed doctors. Communication, local induction and feeling valued and being part of a team helped reduce this. © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

Automatic detection of COVID-19 from chest radiographs using deep learning.

INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths and breadths of the globe, taking a death toll to nearly 700 k by the start of August 2020. The number is well expected to rise even more significantly. In the absence of a thoroughly tested and approved vaccine, the onus primarily lies on obliging to standard operating procedures and timely detection and isolation of the infected persons. The detection of SARS n-CoV2 has been one of the core concerns during the fight against this pandemic. To keep up with the scale of the outbreak, testing needs to be scaled at par with it. With the conventional PCR testing, most of the countries have struggled to minimize the gap between the scale of outbreak and scale of testing. METHOD: One way of expediting the scale of testing is to shift to a rigorous computational model driven by deep neural networks, as proposed here in this paper. The proposed model is a non-contact process of determining whether a subject is infected or not and is achieved by using chest radiographs; one of the most widely used imaging technique for clinical diagnosis due to fast imaging and low cost. The dataset used in this work contains 1428 chest radiographs with confirmed COVID-19 positive, common bacterial pneumonia, and healthy cases (no infection). We explored the pre-trained VGG-16 model for classification tasks in this. Transfer learning with fine-tuning was used in this study to train the network on relatively small chest radiographs effectively. RESULTS: Initial experiments showed that the model achieved promising results and can be significantly used to expedite COVID-19 detection. The experimentation showed an accuracy of 96% and 92.5% in two and three output class cases, respectively. CONCLUSION: We believe that this study could be used as an initial screening, which can help healthcare professionals to treat the COVID patients by timely detecting better and screening the presence of disease. IMPLICATION FOR PRACTICE: Its simplicity drives the proposed deep neural network model, the capability to work on small image dataset, the non-contact method with acceptable accuracy is a potential alternative for rapid COVID-19 testing that can be adapted by the medical fraternity considering the criticality of the time along with the magnitudes of the outbreak.